Exercise

# Assessing simple linear model fit

Recall that the coefficient of determination (\(R^2\)), can be computed as $$ R^2 = 1 - \frac{SSE}{SST} = 1 - \frac{Var(e)}{Var(y)} \,, $$ where \(e\) is the vector of residuals and \(y\) is the response variable. This gives us the interpretation of \(R^2\) as the percentage of the variability in the response that is explained by the model, since the residuals are the part of that variability that remains unexplained by the model.

Instructions

**100 XP**

The `bdims_tidy`

data frame is the result of `augment()`

-ing the `bdims`

data frame with the `mod`

for `wgt`

as a function of `hgt`

.

- Use the
`summary()`

function to view the full results of`mod`

. - Use the
`bdims_tidy`

data frame to compute the \(R^2\) of`mod`

manually using the formula above, by computing the ratio of the variance of the residuals to the variance of the response variable.